Privacy-preserving data analysis using a memristor chip with colocated authentication and processing

Privacy-preserving data analysis is essential in health care applications to safeguard sensitive patient information while enabling medical monitoring and diagnostics. However, existing solutions generally separate security from analysis modules and memory from computation units, creating hardware and energy overheads that constrain their use in resource-limited medical devices. Here, we introduce the memristor-based colocated authentication and processing (CLAP) system, which achieves security-analysis integration through embedding physical unclonable functions within compute-in-memory architecture. To resolve the incompatibilities between these two features, we propose a differential stochastic mapping method by applying information theory principles. We demonstrate CLAP on a 130-nanometer memristor chip, validating its versatility across diverse information processing tasks. In an electrocardiogram data collection task, CLAP achieves device authentication with an area under the curve of 99.46% and efficient signal compression with a software-level percentage root mean square difference. CLAP demonstrates 146.0-fold energy efficiency gain and 17.6-fold area reduction, providing intrinsically secure hardware solutions that enhance both privacy preservation and computational efficiency for health care applications.